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In this study, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multi-objective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a three-dimensional vector, where the factory assignment, machine assignment, operation scheduling and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global and local search procedures. Next, an efficient initialization approach is presented, which embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

Distributed scheduling problem, a novel model of intelligent manufacturing, urgently needs new scheduling methods to meet the dynamic market demand. The inverse scheduling in a distributed shop greatly impacts both its energy consumption and productivity. This paper proposes the energy-efficient distributed permutation flow-shop inverse scheduling problem to minimize adjustment and energy consumption simultaneously. This model contains some realistic constraints, controllable processing times and energy consumption factors. To solve the problem effectively, an effective hybrid collaborative algorithm with cooperative search scheme is designed. The heuristic method and random method are improved to initialize the population. In order to balance the global exploration and local development ability of the algorithm, a double-population cooperative search link based on learning mechanism is presented. Moreover, a dual-mode local search based on switching mechanism is addressed. The influence of key parameters on the performance of the algorithm is proved by using the ANOVA of design-of-experiment. Finally, the developed algorithm has been compared with other approaches for distributed inverse scheduling and assessed by satisfactory results on different set of distributed inverse scheduling problems.

Technology Evolutionary Process have own rule and model,it can be forcasted. How to predict the future technological development and quickly develop next-generation products has become a powerful weapon of market competition. TRIZ EvolutionTheory is one of the most advantages and vitality in almost product technology prediction theory. This article summarized the evolution mode and evolution route based on in-depth study of TRIZ evolution theory ,and researched correlation.At last this article introduced how to use the basic principles of evolutionary theory to solve practical problems of the process approach. And to farming with the plow as an example a typical product design, to have been verified.